Graph attention networks for credit card fraud detection: A relational learning approach

Credit cards have proliferated across the financial sector, enhancing accessibility but also creating new targets for fraud. Fraudsters often use subtle, coordinated techniques that are difficult to detect in isolation. This makes credit card fraud detection a suitable task for Graph Neural Networks (GNNs), which can model and analyze the relationships between entities like users, transactions, and devices. In this paper, we apply Graph Attention Networks (GAT) to a simulated credit card transaction dataset to detect fraudulent transactions. We show that they outperform traditional methods by leveraging relational patterns in the data when trained on the same features. Our findings highlight the promise of GNNs for financial fraud detection, particularly in uncovering complex, hidden connections that are not apparent through standalone analysis.